






import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


UpperCamelCase								=		"bert-base-cased"
UpperCamelCase								=		"google/pegasus-xsum"
UpperCamelCase								=		[" Sam ate lunch today.", "Sams lunch ingredients."]
UpperCamelCase								=		["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
UpperCamelCase								=		"patrickvonplaten/t5-tiny-random"
UpperCamelCase								=		"sshleifer/bart-tiny-random"
UpperCamelCase								=		"sshleifer/tiny-mbart"
UpperCamelCase								=		"sshleifer/tiny-marian-en-de"






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    int:
							_lowercase							:   List[Any] 							=   '\n'.join(SCREAMING_SNAKE_CASE				)
							Path(SCREAMING_SNAKE_CASE				).open('w'				).writelines(SCREAMING_SNAKE_CASE				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Optional[int]:
							for split in ["train", "val", "test"]:
														_dump_articles(os.path.join(SCREAMING_SNAKE_CASE     ,     F"""{split}.source"""				)     ,     SCREAMING_SNAKE_CASE				)
														_dump_articles(os.path.join(SCREAMING_SNAKE_CASE     ,     F"""{split}.target"""				)     ,     SCREAMING_SNAKE_CASE				)
							return tmp_dir






class 				lowerCAmelCase_	(		__snake_case			):


				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ] ,						)
				@slow
				def 						__a					(					self ,						_lowerCAmelCase      ):
											_lowercase							:   int 							=   AutoTokenizer.from_pretrained(_lowerCAmelCase      )
											_lowercase							:   List[Any] 							=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
											_lowercase							:   Dict 							=   max(len(tokenizer.encode(_lowerCAmelCase      )      ) for a in ARTICLES      )
											_lowercase							:   List[Any] 							=   max(len(tokenizer.encode(_lowerCAmelCase      )      ) for a in SUMMARIES      )
											_lowercase							:   List[str] 							=   4
											_lowercase							:   Union[str, Any] 							=   8
											assert max_len_target > max_src_len  # Will be truncated
											assert max_len_source > max_src_len  # Will be truncated
											_lowercase							,							_lowercase							:   str 							=   'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
											_lowercase							:   List[str] 							=   SeqaSeqDataset(
											    _lowerCAmelCase ,						data_dir=_lowerCAmelCase ,						type_path='train' ,						max_source_length=_lowerCAmelCase ,						max_target_length=_lowerCAmelCase ,						src_lang=_lowerCAmelCase ,						tgt_lang=_lowerCAmelCase ,						)
											_lowercase							:   List[str] 							=   DataLoader(_lowerCAmelCase ,						batch_size=2 ,						collate_fn=train_dataset.collate_fn      )
											for batch in dataloader:
																		assert isinstance(_lowerCAmelCase ,						_lowerCAmelCase      )
																		assert batch["attention_mask"].shape == batch["input_ids"].shape
																		# show that articles were trimmed.
																		assert batch["input_ids"].shape[1] == max_src_len
																		# show that targets are the same len
																		assert batch["labels"].shape[1] == max_tgt_len
																		if tok_name != MBART_TINY:
																									continue
																		# check language codes in correct place
																		_lowercase							:   List[str] 							=   shift_tokens_right(batch['labels'] ,						tokenizer.pad_token_id      )
																		assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
																		assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
																		assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
																		assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

																		break  # No need to test every batch


				@parameterized.expand([BART_TINY, BERT_BASE_CASED]      )
				def 						__a					(					self ,						_lowerCAmelCase      ):
											_lowercase							:   Tuple 							=   AutoTokenizer.from_pretrained(_lowerCAmelCase      )
											_lowercase							:   Union[str, Any] 							=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
											_lowercase							:   Union[str, Any] 							=   max(len(tokenizer.encode(_lowerCAmelCase      )      ) for a in ARTICLES      )
											_lowercase							:   Optional[int] 							=   max(len(tokenizer.encode(_lowerCAmelCase      )      ) for a in SUMMARIES      )
											_lowercase							:   Tuple 							=   4
											_lowercase							:   Optional[Any] 							=   LegacySeqaSeqDataset(
											    _lowerCAmelCase ,						data_dir=_lowerCAmelCase ,						type_path='train' ,						max_source_length=2_0 ,						max_target_length=_lowerCAmelCase ,						)
											_lowercase							:   int 							=   DataLoader(_lowerCAmelCase ,						batch_size=2 ,						collate_fn=train_dataset.collate_fn      )
											for batch in dataloader:
																		assert batch["attention_mask"].shape == batch["input_ids"].shape
																		# show that articles were trimmed.
																		assert batch["input_ids"].shape[1] == max_len_source
																		assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
																		# show that targets were truncated
																		assert batch["labels"].shape[1] == trunc_target  # Truncated
																		assert max_len_target > trunc_target  # Truncated
																		break  # No need to test every batch


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'      )

											_lowercase							:   Any 							=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
											_lowercase							:   Optional[int] 							=   tmp_dir.joinpath('train.source'      ).open().readlines()
											_lowercase							:   int 							=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
											pack_data_dir(_lowerCAmelCase ,						_lowerCAmelCase ,						1_2_8 ,						_lowerCAmelCase      )
											_lowercase							:   Optional[int] 							=   {x.name for x in tmp_dir.iterdir()}
											_lowercase							:   List[str] 							=   {x.name for x in save_dir.iterdir()}
											_lowercase							:   List[Any] 							=   save_dir.joinpath('train.source'      ).open().readlines()
											# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
											# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
											assert len(_lowerCAmelCase      ) < len(_lowerCAmelCase      )
											assert len(_lowerCAmelCase      ) == 1
											assert len(packed_examples[0]      ) == sum(len(_lowerCAmelCase      ) for x in orig_examples      )
											assert orig_paths == new_paths


				@pytest.mark.skipif(not FAIRSEQ_AVAILABLE ,						reason='This test requires fairseq'      )
				def 						__a					(					self      ):
											if not FAIRSEQ_AVAILABLE:
																		return
											_lowercase							,							_lowercase							,							_lowercase							:   Union[str, Any] 							=   self._get_dataset(max_len=6_4      )
											_lowercase							:   Dict 							=   6_4
											_lowercase							:   int 							=   ds.make_dynamic_sampler(_lowerCAmelCase ,						required_batch_size_multiple=_lowerCAmelCase      )
											_lowercase							:   Optional[int] 							=   [len(_lowerCAmelCase      ) for x in batch_sampler]
											assert len(set(_lowerCAmelCase      )      ) > 1  # it's not dynamic batch size if every batch is the same length
											assert sum(_lowerCAmelCase      ) == len(_lowerCAmelCase      )  # no dropped or added examples
											_lowercase							:   Tuple 							=   DataLoader(_lowerCAmelCase ,						batch_sampler=_lowerCAmelCase ,						collate_fn=ds.collate_fn ,						num_workers=2      )
											_lowercase							:   Tuple 							=   []
											_lowercase							:   Union[str, Any] 							=   []
											for batch in data_loader:
																		_lowercase							:   Union[str, Any] 							=   batch['input_ids'].shape
																		_lowercase							:   Tuple 							=   src_shape[0]
																		assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
																		_lowercase							:   str 							=   np.product(batch['input_ids'].shape      )
																		num_src_per_batch.append(_lowerCAmelCase      )
																		if num_src_tokens > (max_tokens * 1.1):
																									failures.append(_lowerCAmelCase      )
											assert num_src_per_batch[0] == max(_lowerCAmelCase      )
											if failures:
																		raise AssertionError(F"""too many tokens in {len(_lowerCAmelCase      )} batches"""      )


				def 						__a					(					self      ):
											_lowercase							,							_lowercase							,							_lowercase							:   Tuple 							=   self._get_dataset(max_len=5_1_2      )
											_lowercase							:   Any 							=   2
											_lowercase							:   Optional[int] 							=   ds.make_sortish_sampler(_lowerCAmelCase ,						shuffle=_lowerCAmelCase      )

											_lowercase							:   List[str] 							=   DataLoader(_lowerCAmelCase ,						batch_size=_lowerCAmelCase ,						collate_fn=ds.collate_fn ,						num_workers=2      )
											_lowercase							:   Optional[Any] 							=   DataLoader(_lowerCAmelCase ,						batch_size=_lowerCAmelCase ,						collate_fn=ds.collate_fn ,						num_workers=2 ,						sampler=_lowerCAmelCase      )

											_lowercase							:   str 							=   tokenizer.pad_token_id

											def count_pad_tokens(_lowerCAmelCase ,						_lowerCAmelCase="input_ids"      ):
																		return [batch[k].eq(_lowerCAmelCase      ).sum().item() for batch in data_loader]

											assert sum(count_pad_tokens(_lowerCAmelCase ,						k='labels'      )      ) < sum(count_pad_tokens(_lowerCAmelCase ,						k='labels'      )      )
											assert sum(count_pad_tokens(_lowerCAmelCase      )      ) < sum(count_pad_tokens(_lowerCAmelCase      )      )
											assert len(_lowerCAmelCase      ) == len(_lowerCAmelCase      )


				def 						__a					(					self ,						_lowerCAmelCase=1_0_0_0 ,						_lowerCAmelCase=1_2_8      ):
											if os.getenv('USE_REAL_DATA' ,						_lowerCAmelCase      ):
																		_lowercase							:   Tuple 							=   'examples/seq2seq/wmt_en_ro'
																		_lowercase							:   Optional[Any] 							=   max_len * 2 * 6_4
																		if not Path(_lowerCAmelCase      ).joinpath('train.len'      ).exists():
																									save_len_file(_lowerCAmelCase ,						_lowerCAmelCase      )
											else:
																		_lowercase							:   int 							=   'examples/seq2seq/test_data/wmt_en_ro'
																		_lowercase							:   Any 							=   max_len * 4
																		save_len_file(_lowerCAmelCase ,						_lowerCAmelCase      )

											_lowercase							:   Optional[int] 							=   AutoTokenizer.from_pretrained(_lowerCAmelCase      )
											_lowercase							:   Dict 							=   SeqaSeqDataset(
											    _lowerCAmelCase ,						data_dir=_lowerCAmelCase ,						type_path='train' ,						max_source_length=_lowerCAmelCase ,						max_target_length=_lowerCAmelCase ,						n_obs=_lowerCAmelCase ,						)
											return ds, max_tokens, tokenizer


				def 						__a					(					self      ):
											_lowercase							,							_lowercase							,							_lowercase							:   List[str] 							=   self._get_dataset()
											_lowercase							:   List[str] 							=   set(DistributedSortishSampler(_lowerCAmelCase ,						2_5_6 ,						num_replicas=2 ,						rank=0 ,						add_extra_examples=_lowerCAmelCase      )      )
											_lowercase							:   Tuple 							=   set(DistributedSortishSampler(_lowerCAmelCase ,						2_5_6 ,						num_replicas=2 ,						rank=1 ,						add_extra_examples=_lowerCAmelCase      )      )
											assert idsa.intersection(_lowerCAmelCase      ) == set()


				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ] ,						)
				def 						__a					(					self ,						_lowerCAmelCase      ):
											_lowercase							:   Union[str, Any] 							=   AutoTokenizer.from_pretrained(_lowerCAmelCase ,						use_fast=_lowerCAmelCase      )
											if tok_name == MBART_TINY:
																		_lowercase							:   Dict 							=   SeqaSeqDataset(
																		    _lowerCAmelCase ,						data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      ) ,						type_path='train' ,						max_source_length=4 ,						max_target_length=8 ,						src_lang='EN' ,						tgt_lang='FR' ,						)
																		_lowercase							:   Dict 							=   train_dataset.dataset_kwargs
																		assert "src_lang" in kwargs and "tgt_lang" in kwargs
											else:
																		_lowercase							:   int 							=   SeqaSeqDataset(
																		    _lowerCAmelCase ,						data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      ) ,						type_path='train' ,						max_source_length=4 ,						max_target_length=8 ,						)
																		_lowercase							:   Any 							=   train_dataset.dataset_kwargs
																		assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
																		assert len(_lowerCAmelCase      ) == 1 if tok_name == BART_TINY else len(_lowerCAmelCase      ) == 0












"""simple docstring"""
import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
snake_case : Optional[Any]     		=		"""."""

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
snake_case : List[str]     		=		[
    """Assert""",
    """AssignVariableOp""",
    """EmptyTensorList""",
    """MergeV2Checkpoints""",
    """ReadVariableOp""",
    """ResourceGather""",
    """RestoreV2""",
    """SaveV2""",
    """ShardedFilename""",
    """StatefulPartitionedCall""",
    """StaticRegexFullMatch""",
    """VarHandleOp""",
]


def 							A						(				__snake_case: Optional[Any] ,     __snake_case: Tuple ,     __snake_case: Any  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    SavedModel()
       __magic_name__      				=    []

       with open(os.path.join(__snake_case ,     'utils' ,     'tf_ops' ,     'onnx.json'  )  ) as f:
              __magic_name__      				=    json.load(__snake_case  )['opsets']

       for i in range(1 ,     opset + 1  ):
              onnx_ops.extend(onnx_opsets[str(__snake_case  )]  )

       with open(__snake_case ,     'rb'  ) as f:
              saved_model.ParseFromString(f.read()  )

       __magic_name__      				=    set()

       # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
       for meta_graph in saved_model.meta_graphs:
              # Add operations in the graph definition
              model_op_names.update(node.op for node in meta_graph.graph_def.node  )

              # Go through the functions in the graph definition
              for func in meta_graph.graph_def.library.function:
                     # Add operations in each function
                     model_op_names.update(node.op for node in func.node_def  )

    # Convert to list, sorted if you want
       __magic_name__      				=    sorted(__snake_case  )
       __magic_name__      				=    []

       for op in model_op_names:
              if op not in onnx_ops and op not in INTERNAL_OPS:
                     incompatible_ops.append(__snake_case  )

       if strict and len(__snake_case  ) > 0:
              raise Exception(F"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops  )
       elif len(__snake_case  ) > 0:
              print(F"""Found the following incompatible ops for the opset {opset}:"""  )
              print(*__snake_case ,     sep='\n'  )
       else:
              print(F"""The saved model {saved_model_path} can properly be converted with ONNX."""  )


if __name__ == "__main__":
 snake_case : Optional[Any]     		=		argparse.ArgumentParser()
 parser.add_argument("""--saved_model_path""", help="""Path of the saved model to check (the .pb file).""")
 parser.add_argument(
     """--opset""", default=1_2, type=int, help="""The ONNX opset against which the model has to be tested."""
 )
 parser.add_argument(
     """--framework""", choices=["""onnx"""], default="""onnx""", help="""Frameworks against which to test the saved model."""
 )
 parser.add_argument(
     """--strict""", action="""store_true""", help="""Whether make the checking strict (raise errors) or not (raise warnings)"""
 )
 snake_case : Any     		=		parser.parse_args()

 if args.framework == "onnx":
  onnx_compliancy(args.saved_model_path, args.strict, args.opset)